

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast



@require_vision
class        A__(unittest.TestCase							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						List[Any] 							= tempfile.mkdtemp()

									a_						:						Tuple 							= BlipImageProcessor()
									a_						:						List[str] 							= BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""    )

									a_						:						List[Any] 							= BlipProcessor(_lowercase  ,      _lowercase    )

									processor.save_pretrained(self.tmpdirname    )





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> int:
									return AutoProcessor.from_pretrained(self.tmpdirname  ,      **_lowercase    ).tokenizer





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> Optional[int]:
									return AutoProcessor.from_pretrained(self.tmpdirname  ,      **_lowercase    ).image_processor





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									shutil.rmtree(self.tmpdirname    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[Any] 							= [np.random.randint(255  ,      size=(3, 30, 400)  ,      dtype=np.uinta    )]

									a_						:						int 							= [Image.fromarray(np.moveaxis(_lowercase  ,      0  ,      -1    )    ) for x in image_inputs]

									return image_inputs





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						Union[str, Any] 							= BlipProcessor(tokenizer=self.get_tokenizer()  ,      image_processor=self.get_image_processor()    )
									processor.save_pretrained(self.tmpdirname    )

									a_						:						Optional[int] 							= self.get_tokenizer(bos_token="""(BOS)"""  ,      eos_token="""(EOS)"""    )
									a_						:						int 							= self.get_image_processor(do_normalize=_lowercase  ,      padding_value=1.0    )

									a_						:						List[str] 							= BlipProcessor.from_pretrained(
									    self.tmpdirname  ,      bos_token="""(BOS)"""  ,      eos_token="""(EOS)"""  ,      do_normalize=_lowercase  ,      padding_value=1.0    )

									self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()    )
									self.assertIsInstance(processor.tokenizer  ,      _lowercase    )

									self.assertEqual(processor.image_processor.to_json_string()  ,      image_processor_add_kwargs.to_json_string()    )
									self.assertIsInstance(processor.image_processor  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						List[str] 							= self.get_image_processor()
									a_						:						Dict 							= self.get_tokenizer()

									a_						:						List[Any] 							= BlipProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						Optional[Any] 							= self.prepare_image_inputs()

									a_						:						Optional[Any] 							= image_processor(_lowercase  ,      return_tensors="""np"""    )
									a_						:						Any 							= processor(images=_lowercase  ,      return_tensors="""np"""    )

									for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()  ,      input_processor[key].sum()  ,      delta=1e-2    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[Any] 							= self.get_image_processor()
									a_						:						Dict 							= self.get_tokenizer()

									a_						:						List[str] 							= BlipProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						List[Any] 							= """lower newer"""

									a_						:						Any 							= processor(text=_lowercase    )

									a_						:						Union[str, Any] 							= tokenizer(_lowercase  ,      return_token_type_ids=_lowercase    )

									for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Union[str, Any] 							= self.get_image_processor()
									a_						:						int 							= self.get_tokenizer()

									a_						:						Optional[Any] 							= BlipProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						List[Any] 							= """lower newer"""
									a_						:						Optional[Any] 							= self.prepare_image_inputs()

									a_						:						int 							= processor(text=_lowercase  ,      images=_lowercase    )

									self.assertListEqual(list(inputs.keys()    )  ,      ["""pixel_values""", """input_ids""", """attention_mask"""]    )

									# test if it raises when no input is passed
									with pytest.raises(_lowercase    ):
												processor()





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Union[str, Any] 							= self.get_image_processor()
									a_						:						List[Any] 							= self.get_tokenizer()

									a_						:						Union[str, Any] 							= BlipProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						int 							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									a_						:						Optional[Any] 							= processor.batch_decode(_lowercase    )
									a_						:						Optional[Any] 							= tokenizer.batch_decode(_lowercase    )

									self.assertListEqual(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Optional[Any] 							= self.get_image_processor()
									a_						:						int 							= self.get_tokenizer()

									a_						:						List[Any] 							= BlipProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						int 							= """lower newer"""
									a_						:						List[str] 							= self.prepare_image_inputs()

									a_						:						Any 							= processor(text=_lowercase  ,      images=_lowercase    )

									# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
									self.assertListEqual(list(inputs.keys()    )  ,      ["""pixel_values""", """input_ids""", """attention_mask"""]    )





from __future__ import annotations


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						List[str] 							= 0
			a_						:						str 							= len(a__) - 1

			while i < j:
						if nums[i] + nums[j] == target:
									return [i, j]
						elif nums[i] + nums[j] < target:
									a_						:						List[Any] 							= i + 1
						else:
									a_						:						Optional[Any] 							= j - 1

			return []


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")


